
"""simple docstring"""


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[Any]:
							A     = self.config_class(**self.inputs_dict   )
							self.parent.assertTrue(hasattr(A_	,'tf_padding'   )   )
							self.parent.assertTrue(hasattr(A_	,'depth_multiplier'   )   )







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Optional[int]	,A_  :   Optional[int]=13	,A_  :   Tuple=3	,A_  :   Any=32	,A_  :   Optional[Any]=0.25	,A_  :   Union[str, Any]=8	,A_  :   List[str]=True	,A_  :   List[Any]=1024	,A_  :   str=32	,A_  :   Tuple="relu6"	,A_  :   Optional[int]=0.1	,A_  :   int=0.02	,A_  :   int=True	,A_  :   Any=True	,A_  :   Optional[int]=10	,A_  :   List[Any]=None	,)  -> List[Any]:
							A     = parent
							A     = batch_size
							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = tf_padding
							A     = int(last_hidden_size * depth_multiplier   )
							A     = output_stride
							A     = hidden_act
							A     = classifier_dropout_prob
							A     = use_labels
							A     = is_training
							A     = num_labels
							A     = initializer_range
							A     = scope


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Any:
							A     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							A     = None
							A     = None
							if self.use_labels:
								A     = ids_tensor([self.batch_size]	,self.num_labels   )
								A     = ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels   )

							A     = self.get_config()

							return config, pixel_values, labels, pixel_labels


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[Any]:
							return MobileNetVaConfig(
							    num_channels=self.num_channels	,image_size=self.image_size	,depth_multiplier=self.depth_multiplier	,min_depth=self.min_depth	,tf_padding=self.tf_padding	,hidden_act=self.hidden_act	,classifier_dropout_prob=self.classifier_dropout_prob	,initializer_range=self.initializer_range	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   int	,A_  :   List[Any]	,A_  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = MobileNetVaModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,(
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   int	,A_  :   Dict	,A_  :   Optional[Any]	,A_  :   Union[str, Any]   )  -> Tuple:
							A     = self.num_labels
							A     = MobileNetVaForImageClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,labels=A_   )
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Optional[Any]:
							A     = self.prepare_config_and_inputs()
							A ,   A ,   A ,   A     = config_and_inputs
							A     = {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
						_lowerCamelCase:     Any   =     (
						    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_lowerCamelCase:     List[str]   =     False
						_lowerCamelCase:     Optional[Any]   =     False
						_lowerCamelCase:     List[Any]   =     False
						_lowerCamelCase:     Union[str, Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> str:
							A     = MobileNetVaModelTester(self   )
							A     = MobileNetVaConfigTester(self	,config_class=A_	,has_text_modality=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							self.config_tester.run_common_tests()


						@unittest.skip(reason='MobileNetV1 does not use inputs_embeds'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Union[str, Any]:
							pass


						@unittest.skip(reason='MobileNetV1 does not support input and output embeddings'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							pass


						@unittest.skip(reason='MobileNetV1 does not output attentions'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> List[str]:
							pass


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A     = model_class(A_   )
								A     = inspect.signature(model.forward   )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								A     = [*signature.parameters.keys()]

								A     = ['pixel_values']
								self.assertListEqual(arg_names[:1]	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Tuple:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[Any]:
							def check_hidden_states_output(A_  :   int	,A_  :   Dict	,A_  :   str   ):
								A     = model_class(A_   )
								model.to(A_   )
								model.eval()

								with torch.no_grad():
									A     = model(**self._prepare_for_class(A_	,A_   )   )

								A     = outputs.hidden_states

								A     = 26
								self.assertEqual(len(A_   )	,A_   )

							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A     = True
								check_hidden_states_output(A_	,A_	,A_   )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								A     = True

								check_hidden_states_output(A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[str]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> int:
							for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = MobileNetVaModel.from_pretrained(A_   )
								self.assertIsNotNone(A_   )






def    _snake_case					(		):
	A     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







@require_torch
@require_vision
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict:
							return (
							    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224'   ) if is_vision_available() else None
							)




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> str:
							A     = MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224'   ).to(A_   )

							A     = self.default_image_processor
							A     = prepare_img()
							A     = image_processor(images=A_	,return_tensors='pt'   ).to(A_   )

							# forward pass
							with torch.no_grad():
								A     = model(**A_   )

							# verify the logits
							A     = torch.Size((1, 1001)   )
							self.assertEqual(outputs.logits.shape	,A_   )

							A     = torch.tensor([-4.17_39, -1.12_33, 3.12_05]   ).to(A_   )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]	,A_	,atol=1e-4   )   )







'''simple docstring'''





import collections
import os
import re
from pathlib import Path


_UpperCamelCase						:						Optional[int]  =					'src/transformers'


# Matches is_xxx_available()
_UpperCamelCase						:						int  =					re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_UpperCamelCase						:						Dict  =					re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_UpperCamelCase						:						List[str]  =					re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
_UpperCamelCase						:						Any  =					re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
_UpperCamelCase						:						Dict  =					re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_UpperCamelCase						:						Tuple  =					re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
_UpperCamelCase						:						Union[str, Any]  =					re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_UpperCamelCase						:						str  =					re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
_UpperCamelCase						:						Union[str, Any]  =					re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
_UpperCamelCase						:						Optional[int]  =					re.compile(R'^\s*try:')
# Catches a line with else:
_UpperCamelCase						:						List[Any]  =					re.compile(R'^\s*else:')
def __UpperCAmelCase   (					A					:			Any		)    ->   Tuple:
			if _re_test_backend.search(A		) is None:
						return None
			UpperCAmelCase_      :       str          =       [b[0] for b in _re_backend.findall(A		)]
			backends.sort()
			return "_and_".join(A		)
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[int]:
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       str          =       f.readlines()

			UpperCAmelCase_      :       List[str]          =       0
			while line_index < len(A		) and not lines[line_index].startswith('''_import_structure = {'''		):
						line_index += 1

			# If this is a traditional init, just return.
			if line_index >= len(A		):
						return None

			# First grab the objects without a specific backend in _import_structure
			UpperCAmelCase_      :       Tuple          =       []
			while not lines[line_index].startswith('''if TYPE_CHECKING'''		) and find_backend(lines[line_index]		) is None:
						UpperCAmelCase_      :       List[Any]          =       lines[line_index]
						# If we have everything on a single line, let's deal with it.
						if _re_one_line_import_struct.search(A		):
									UpperCAmelCase_      :       str          =       _re_one_line_import_struct.search(A		).groups()[0]
									UpperCAmelCase_      :       Any          =       re.findall(r'''\[([^\]]+)\]'''			,       A		)
									for imp in imports:
												objects.extend([obj[1:-1] for obj in imp.split(''', '''		)]		)
									line_index += 1
									continue
						UpperCAmelCase_      :       Dict          =       _re_import_struct_key_value.search(A		)
						if single_line_import_search is not None:
									UpperCAmelCase_      :       List[str]          =       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''		) if len(A		) > 0]
									objects.extend(A		)
						elif line.startswith(''' ''' * 8 + '''"'''		):
									objects.append(line[9:-3]		)
						line_index += 1

			UpperCAmelCase_      :       int          =       {'''none''': objects}
			# Let's continue with backend-specific objects in _import_structure
			while not lines[line_index].startswith('''if TYPE_CHECKING'''		):
						# If the line is an if not is_backend_available, we grab all objects associated.
						UpperCAmelCase_      :       Any          =       find_backend(lines[line_index]		)
						# Check if the backend declaration is inside a try block:
						if _re_try.search(lines[line_index - 1]		) is None:
									UpperCAmelCase_      :       int          =       None

						if backend is not None:
									line_index += 1

									# Scroll until we hit the else block of try-except-else
									while _re_else.search(lines[line_index]		) is None:
												line_index += 1

									line_index += 1

									UpperCAmelCase_      :       str          =       []
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 4		):
												UpperCAmelCase_      :       Optional[Any]          =       lines[line_index]
												if _re_import_struct_add_one.search(A		) is not None:
															objects.append(_re_import_struct_add_one.search(A		).groups()[0]		)
												elif _re_import_struct_add_many.search(A		) is not None:
															UpperCAmelCase_      :       Dict          =       _re_import_struct_add_many.search(A		).groups()[0].split(''', '''		)
															UpperCAmelCase_      :       Dict          =       [obj[1:-1] for obj in imports if len(A		) > 0]
															objects.extend(A		)
												elif _re_between_brackets.search(A		) is not None:
															UpperCAmelCase_      :       int          =       _re_between_brackets.search(A		).groups()[0].split(''', '''		)
															UpperCAmelCase_      :       List[Any]          =       [obj[1:-1] for obj in imports if len(A		) > 0]
															objects.extend(A		)
												elif _re_quote_object.search(A		) is not None:
															objects.append(_re_quote_object.search(A		).groups()[0]		)
												elif line.startswith(''' ''' * 8 + '''"'''		):
															objects.append(line[9:-3]		)
												elif line.startswith(''' ''' * 1_2 + '''"'''		):
															objects.append(line[1_3:-3]		)
												line_index += 1

									UpperCAmelCase_      :       int          =       objects
						else:
									line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
			UpperCAmelCase_      :       Union[str, Any]          =       []
			while (
			    line_index < len(A		)
			    and find_backend(lines[line_index]		) is None
			    and not lines[line_index].startswith('''else'''		)
			):
						UpperCAmelCase_      :       int          =       lines[line_index]
						UpperCAmelCase_      :       int          =       _re_import.search(A		)
						if single_line_import_search is not None:
									objects.extend(single_line_import_search.groups()[0].split(''', '''		)		)
						elif line.startswith(''' ''' * 8		):
									objects.append(line[8:-2]		)
						line_index += 1

			UpperCAmelCase_      :       int          =       {'''none''': objects}
			# Let's continue with backend-specific objects
			while line_index < len(A		):
						# If the line is an if is_backend_available, we grab all objects associated.
						UpperCAmelCase_      :       Optional[int]          =       find_backend(lines[line_index]		)
						# Check if the backend declaration is inside a try block:
						if _re_try.search(lines[line_index - 1]		) is None:
									UpperCAmelCase_      :       Optional[int]          =       None

						if backend is not None:
									line_index += 1

									# Scroll until we hit the else block of try-except-else
									while _re_else.search(lines[line_index]		) is None:
												line_index += 1

									line_index += 1

									UpperCAmelCase_      :       str          =       []
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 8		):
												UpperCAmelCase_      :       Tuple          =       lines[line_index]
												UpperCAmelCase_      :       Tuple          =       _re_import.search(A		)
												if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(''', '''		)		)
												elif line.startswith(''' ''' * 1_2		):
															objects.append(line[1_2:-2]		)
												line_index += 1

									UpperCAmelCase_      :       Optional[Any]          =       objects
						else:
									line_index += 1

			return import_dict_objects, type_hint_objects
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Union[str, Any]		)    ->   Optional[Any]:

			def find_duplicates(A					:			Optional[int]		):
						return [k for k, v in collections.Counter(A		).items() if v > 1]

			if list(import_dict_objects.keys()		) != list(type_hint_objects.keys()		):
						return ["Both sides of the init do not have the same backends!"]

			UpperCAmelCase_      :       str          =       []
			for key in import_dict_objects.keys():
						UpperCAmelCase_      :       Union[str, Any]          =       find_duplicates(import_dict_objects[key]		)
						if duplicate_imports:
									errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"		)
						UpperCAmelCase_      :       Optional[int]          =       find_duplicates(type_hint_objects[key]		)
						if duplicate_type_hints:
									errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"		)

						if sorted(set(import_dict_objects[key]		)		) != sorted(set(type_hint_objects[key]		)		):
									UpperCAmelCase_      :       Optional[int]          =       '''base imports''' if key == '''none''' else F"{key} backend"
									errors.append(F"Differences for {name}:"		)
									for a in type_hint_objects[key]:
												if a not in import_dict_objects[key]:
															errors.append(F"  {a} in TYPE_HINT but not in _import_structure."		)
									for a in import_dict_objects[key]:
												if a not in type_hint_objects[key]:
															errors.append(F"  {a} in _import_structure but not in TYPE_HINT."		)
			return errors
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Tuple          =       []
			for root, _, files in os.walk(A		):
						if "__init__.py" in files:
									UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''__init__.py'''		)
									UpperCAmelCase_      :       str          =       parse_init(A		)
									if objects is not None:
												UpperCAmelCase_      :       List[str]          =       analyze_results(*A		)
												if len(A		) > 0:
															UpperCAmelCase_      :       Optional[Any]          =       F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
															failures.append('''\n'''.join(A		)		)
			if len(A		) > 0:
						raise ValueError('''\n\n'''.join(A		)		)
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       Any          =       []
			for path, directories, files in os.walk(A		):
						for folder in directories:
									# Ignore private modules
									if folder.startswith('''_'''		):
												directories.remove(A		)
												continue
									# Ignore leftovers from branches (empty folders apart from pycache)
									if len(list((Path(A		) / folder).glob('''*.py'''		)		)		) == 0:
												continue
									UpperCAmelCase_      :       Union[str, Any]          =       str((Path(A		) / folder).relative_to(A		)		)
									UpperCAmelCase_      :       List[Any]          =       short_path.replace(os.path.sep			,       '''.'''		)
									submodules.append(A		)
						for fname in files:
									if fname == "__init__.py":
												continue
									UpperCAmelCase_      :       Optional[Any]          =       str((Path(A		) / fname).relative_to(A		)		)
									UpperCAmelCase_      :       Union[str, Any]          =       short_path.replace('''.py'''			,       ''''''		).replace(os.path.sep			,       '''.'''		)
									if len(submodule.split('''.'''		)		) == 1:
												submodules.append(A		)
			return submodules


_UpperCamelCase						:						List[Any]  =					[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]
def __UpperCAmelCase   (					)    ->   int:
			# This is to make sure the transformers module imported is the one in the repo.
			from transformers.utils import direct_transformers_import

			UpperCAmelCase_      :       Dict          =       direct_transformers_import(A		)

			UpperCAmelCase_      :       List[str]          =       set(transformers._import_structure.keys()		)
			# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
			# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
			# (potentiall re-) add them.
			with open(os.path.join(A			,       '''__init__.py'''		)			,       '''r'''		) as f:
						UpperCAmelCase_      :       Dict          =       f.read()
			import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]'''			,       A		)		)		)

			UpperCAmelCase_      :       Union[str, Any]          =       [
			    module
			    for module in get_transformers_submodules()
			    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
			]

			if len(A		) > 0:
						UpperCAmelCase_      :       Optional[Any]          =       '''\n'''.join(F"- {module}" for module in module_not_registered		)
						raise ValueError(
						    '''The following submodules are not properly registed in the main init of Transformers:\n'''
						    F"{list_of_modules}\n"
						    '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''		)


if __name__ == "__main__":
						check_all_inits()
						check_submodules()


